%0 Conference Paper %1 Zhao:2013:CUR:2505515.2505533 %A Zhao, Gang %A Lee, Mong Li %A Hsu, Wynne %A Chen, Wei %A Hu, Haoji %B Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management %C New York, NY, USA %D 2013 %I ACM %K social-information-access %P 189--198 %R 10.1145/2505515.2505533 %T Community-based User Recommendation in Uni-directional Social Networks %U http://doi.acm.org/10.1145/2505515.2505533 %X Advances in Web 2.0 technology has led to the rising popularity of many social network services. For example, there are over 500 million active users in Twitter. Given the huge number of users, user recommendation has gained importance where the goal is to find a set of users whom a target user is likely to follow. Content-based approaches that rely on tweet content for user recommendation have low precision as tweet contents are typically short and noisy, while collaborative filtering approaches that utilize follower-followee relationships lead to higher precision but data sparsity remains a challenge. In this work, we propose a community-based approach to user recommendation in Twitter-style social networks. Forming communities enables us to reduce data sparsity as the focus is on discover the latent characteristics of communities instead of individuals. We employ an LDA-based method on the follower-followee relationships to discover communities before applying the state-of-the-art matrix factorization method on each of the communities. This approach proves effective in improving the conversion rate (by as much as 20%) as demonstrated by the results of extensive experiments on two real world data sets Twitter and Weibo. In addition, the community-based approach is scalable as the individual community can be analyzed separately. %@ 978-1-4503-2263-8

@inproceedings{Zhao:2013:CUR:2505515.2505533, abstract = {Advances in Web 2.0 technology has led to the rising popularity of many social network services. For example, there are over 500 million active users in Twitter. Given the huge number of users, user recommendation has gained importance where the goal is to find a set of users whom a target user is likely to follow. Content-based approaches that rely on tweet content for user recommendation have low precision as tweet contents are typically short and noisy, while collaborative filtering approaches that utilize follower-followee relationships lead to higher precision but data sparsity remains a challenge. In this work, we propose a community-based approach to user recommendation in Twitter-style social networks. Forming communities enables us to reduce data sparsity as the focus is on discover the latent characteristics of communities instead of individuals. We employ an LDA-based method on the follower-followee relationships to discover communities before applying the state-of-the-art matrix factorization method on each of the communities. This approach proves effective in improving the conversion rate (by as much as 20%) as demonstrated by the results of extensive experiments on two real world data sets Twitter and Weibo. In addition, the community-based approach is scalable as the individual community can be analyzed separately.}, acmid = {2505533}, added-at = {2017-04-17T19:49:45.000+0200}, address = {New York, NY, USA}, author = {Zhao, Gang and Lee, Mong Li and Hsu, Wynne and Chen, Wei and Hu, Haoji}, biburl = {https://www.bibsonomy.org/bibtex/2fb68e35474a9306521a7c6b662e88450/fanyang}, booktitle = {Proceedings of the 22Nd ACM International Conference on Information \& Knowledge Management}, description = {Community-based user recommendation in uni-directional social networks}, doi = {10.1145/2505515.2505533}, interhash = {19d0ed90b2895bc0e94730996c0cca0b}, intrahash = {fb68e35474a9306521a7c6b662e88450}, isbn = {978-1-4503-2263-8}, keywords = {social-information-access}, location = {San Francisco, California, USA}, numpages = {10}, pages = {189--198}, publisher = {ACM}, series = {CIKM '13}, timestamp = {2017-04-17T19:49:45.000+0200}, title = {Community-based User Recommendation in Uni-directional Social Networks}, url = {http://doi.acm.org/10.1145/2505515.2505533}, year = 2013 }